Agentic Workflows in Practice

Agentic Workflows in Practice

Common Patterns

Research Pipeline: Search → read pages → extract key information → synthesize → structured report.

Code Review Bot: Triggered by PR → reads changed files → checks style guide and security patterns → posts structured comments via GitHub API.

Automated QA: Reads feature spec → generates tests → writes test code → executes → reads failures → files bug report.

Data Extraction: Receives batch of documents → extracts structured fields → validates against business rules → writes to database.

Design Principles

Break tasks into checkpoints. Define explicit validation points before proceeding. If step 3 produces invalid output, stop and surface the failure — don't silently continue to step 10.

Human-in-the-loop gates. For consequential or irreversible actions, require human approval. A data extraction agent can run automated; one that initiates wire transfers should pause for sign-off.

Idempotent actions. Design every action so running it twice produces the same result as once. Use upsert rather than insert; write to a temp file then rename.

Log inputs and outputs at every step. Agent failures are hard to debug without a full trace.

Case Study: Legal Contract Processing

1. 1.Ingest — Receives PDF, extracts text
2. 2.Classify — Determines contract type (NDA, SaaS, employment)
3. 3.Extract — Pulls parties, effective date, termination clauses, liability caps
4. 4.Validate — Checks against expected formats
5. 5.Checkpoint — Writes to staging; flags low-confidence items for human review
6. 6.Commit — On approval, writes to production database

Result: Hundreds of documents processed per day, humans reviewing fewer than 5%.